1. Field
Embodiments of the present invention relate to a three-dimensional face recognition method based on intermediate frequency information in a geometric image, wherein a pretreated three-dimensional face model is processed by mesh parameterization and linear interpolation to obtain a geometric image, intermediate frequency information images with identity discriminability are extracted from the geometric image as expression invariant features of the three-dimensional face model with a multi-scale Haar wavelet filter, the degree of similarity of intermediate frequency information images between the test model and the library model is calculated with a Wavelet Domain Structure Similarity algorithm to judge the identity of the test model. The intermediate frequency information image in three-dimensional face model set forth in the present invention has high identity representation capability, and effectively decreases the impact of expression variation on three-dimensional face recognition. With a Wavelet Domain Structure Similarity algorithm, the structural information similarity of an intermediate frequency information image between the test model and the library model is calculated accurately, and thereby the recognition rate of the three-dimensional face recognition method is significantly improved.
2. Description of the Related Art
Biometric recognition takes an important role in the security domain; especially, compared with other feature recognition technologies, such as fingerprint recognition and iris recognition, automatic face recognition technology has received more and more attention and has a broad development space, owing to its advantages such as its contactless nature, high acceptability, and high unobtrusiveness, etc.
Conventional photo image based face recognition technology is restricted by factors such as illumination, posture, and make-up, etc.; in contrast, three-dimensional face recognition technology can overcome or mitigate the adverse impacts of these factors. A three-dimensional face model has richer information than a two-dimensional image, and can characterize the true spatial form of a face more accurately. However, a three-dimensional face model involves high data volume, more interference regions, and high computational complexity; in addition, the non-rigid distortion resulted from facial expression may cause degraded performance of a geometric information based three-dimensional face recognition method. Therefore, how to decrease computational complexity and mitigate the impact of facial expression is a bottleneck in the three-dimensional face recognition technology as well as a key challenge in the research.